Aiding Low Cost Inertial Navigation with Building Heading for Pedestrian Navigation

2011 ◽  
Vol 64 (2) ◽  
pp. 219-233 ◽  
Author(s):  
Khairi Abdulrahim ◽  
Chris Hide ◽  
Terry Moore ◽  
Chris Hill
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Sensors ◽  
Low Cost ◽  
Inertial Navigation ◽  
Field Trials ◽  
Basic Knowledge ◽  
Effective Solution ◽  
Pedestrian Navigation

In environments where GNSS is unavailable or not useful for positioning, the use of low cost MEMS-based inertial sensors has paved a way to a more cost effective solution. Of particular interest is a foot mounted pedestrian navigation system, where zero velocity updates (ZUPT) are used with the standard strapdown navigation algorithm in a Kalman filter to restrict the error growth of the low cost inertial sensors. However heading drift still remains despite using ZUPT measurements since the heading error is unobservable. External sensors such as magnetometers are normally used to mitigate this problem, but the reliability of such an approach is questionable because of the existence of magnetic disturbances that are often very difficult to predict. Hence there is a need to eliminate the heading drift problem for such a low cost system without relying on external sensors to give a possible stand-alone low cost inertial navigation system. In this paper, a novel and effective algorithm for generating heading measurements from basic knowledge of the orientation of the building in which the pedestrian is walking is proposed to overcome this problem. The effectiveness of this approach is demonstrated through three field trials using only a forward Kalman filter that can work in real-time without any external sensors. This resulted in position accuracy better than 5 m during a 40 minutes walk, about 0·1% in position error of the total distance. Due to its simplistic algorithm, this simple yet very effective solution is appealing for a promising future autonomous low cost inertial navigation system.

MODELING AND NUMERICAL SIMULATION OF AN ALGORITHM FOR THE INERTIAL SENSORS ERRORS REDUCTION AND FOR THE INCREASE OF THE STRAP-DOWN NAVIGATOR REDUNDANCY DEGREE IN A LOW COST ARCHITECTURE

2010 ◽  
Vol 34 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Lucian T. Grigorie ◽  
Ruxandra M. Botez
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Lower Cost ◽  
Inertial Sensors ◽  
Low Cost ◽  
Inertial Navigation ◽  
Noise Power ◽  
Integration Algorithm ◽  
The Cost ◽  
Miniaturized Sensors

In this paper, an algorithm for the inertial sensors errors reduction in a strap-down inertial navigation system, using several miniaturized inertial sensors for each axis of the vehicle frame, is conceived. The algorithm is based on the idea of the maximum ratio-combined telecommunications method. We consider that it would be much more advantageous to set a high number of miniaturized sensors on each input axis of the strap-down inertial system instead of a single one, more accurate but expensive and with larger dimensions. Moreover, a redundant system, which would isolate any of the sensors in case of its malfunctioning, is obtained. In order to test the algorithm, Simulink code is used for algorithm and for the acceleration inertial sensors modeling. The Simulink resulted sensors models include their real errors, based on the data sheets parameters, and were conceived based on the IEEE analytical standardized accelerometers model. An integration algorithm is obtained, in which the signal noise power delivered to the navigation processor, is reduced, proportionally with the number of the integrated sensors. At the same time, the bias of the resulted signal is reduced, and provides a high redundancy degree for the strap-down inertial navigation system at a lower cost than at the cost of more accurate and expensive sensors.

Compensation for Stochastic Error of Gyros in a Dual-axis Rotational Inertial Navigation System

2015 ◽  
Vol 69 (1) ◽  
pp. 169-182 ◽  
Author(s):  
Zhichao Zheng ◽  
Songlai Han ◽  
Jin Yue ◽  
Linglong Yuan
Keyword(s):  
Navigation System ◽  
High Performance ◽  
Inertial Navigation System ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Low Cost ◽  
Inertial Navigation ◽  
Field Tests ◽  
Stochastic Error ◽  
Stochastic Errors

A dual-axis rotational Inertial Navigation System (INS) has received wide attention in recent years because of high performance and low cost. However, some errors of inertial sensors such as stochastic errors are not averaged out automatically during navigation. Therefore a Twice Position-fix Reset (TPR) method is provided to enhance accuracy of a dual-axis rotational INS by compensating stochastic errors. According to characteristics of an azimuth error introduced by stochastic errors of an inertial sensor in the dual-axis rotational INS, both an azimuth error and a radial-position error are much better corrected by the TPR method based on an optimised error propagation equation. As a result, accuracy of the dual-axis rotational INS is prominently enhanced by the TPR method, as is verified by simulations and field tests.

scholarly journals FPGA Control of a Mobile Inverted Pendulum Robot

1970 ◽  
Vol 8 (1-2) ◽  
pp. 188-196
Author(s):  
Bikram Adhikari ◽  
Deepak Gurung ◽  
Giresh Singh Kunwar ◽  
Prashanta Gyawali
Keyword(s):  
Kalman Filter ◽  
Sensor Fusion ◽  
Pid Control ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inverted Pendulum ◽  
Inertial Sensors ◽  
Inertial Navigation ◽  
Soft Core ◽  
Quadrature Phase

The inverted pendulum is a classic problem in dynamics and control theory due to its inherently unstable nature. In the system tested, Field Programmable Gate Arrays (FPGAs) are used for the implementation of control and sensor fusion algorithms in the inertial navigation system of a Mobile Inverted Pendulum (MIP) robot. Additionally, the performance of digital PID control and Kalman filter algorithms are tested in this FPGA system. The test platform for tuning Kalman filter is designed using optical encoders as a standard reference. PWM signal generation and quadrature phase decoding of encoder pulses is accomplished using hardware description language in FPGA. The values from the inertial sensors and quadrature phase decoded values are fed into MicroBlaze, a 32-bit soft-core RISC processor, within the FPGA. The overall system demonstrates the use of low cost inertial sensors to balance a two wheeled robot. The system is presently able to balance on its own and it also serves as an extremely reconfigurable FPGA based platform to facilitate future modifications, updates and enhancements with more complex control and sensor fusion techniques.Key Terms: Mobile Inverted Pendulum System; Inverted Pendulum Robot; Inertial Navigation System; FPGA; Kalman Filter; PID Control; Soft-core ProcessorDOI: http://dx.doi.org/10.3126/jie.v8i1-2.5111Journal of the Institute of EngineeringVol. 8, No. 1&2, 2010/2011Page: 188-196Uploaded Date: 20 July, 2011

scholarly journals Pedestrian Navigation Method Based on Machine Learning and Gait Feature Assistance

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1530 ◽  
Author(s):  
Zijun Zhou ◽  
Shuqin Yang ◽  
Zhisen Ni ◽  
Weixing Qian ◽  
Cuihong Gu ◽  
...  
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Sensors ◽  
Inertial Navigation ◽  
The Body ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
Pedestrian Navigation ◽  
Gait Feature ◽  
Navigation Method

In recent years, as the mechanical structure of humanoid robots increasingly resembles the human form, research on pedestrian navigation technology has become of great significance for the development of humanoid robot navigation systems. To solve the problem that the wearable inertial navigation system based on micro-inertial measurement units (MIMUs) installed on feet cannot effectively realize its positioning function when the body movement is too drastic to be measured correctly by commercial grade inertial sensors, a pedestrian navigation method based on construction of a virtual inertial measurement unit (VIMU) and gait feature assistance is proposed. The inertial data from different positions of pedestrians’ lower limbs are collected synchronously via actual IMUs as training samples. The nonlinear mapping relationship between inertial information from the human foot and leg is established by a visual geometry group-long short term memory (VGG-LSTM) neural network model, based on which the foot VIMU and virtual inertial navigation system (VINS) are constructed. The VINS experimental results show that, combined with zero-velocity update (ZUPT), the integrated method of error modification proposed in this paper can effectively reduce the accumulation of positioning errors in situations where the gait type exceeds the measurement range of the inertial sensors. The positioning performance of the proposed method is more accurate and stable in complex gait types than that merely using ZUPT.

An Improved and Efficient Algorithm for SINS/GPS/Doppler Integrated Navigation Systems

2012 ◽  
Vol 245 ◽  
pp. 323-329 ◽  
Author(s):  
Muhammad Ushaq ◽  
Jian Cheng Fang
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Sensor ◽  
Inertial Navigation ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Efficient Manner ◽  
Position Errors

Inertial navigation systems exhibit position errors that tend to grow with time in an unbounded mode. This degradation is due, in part, to errors in the initialization of the inertial measurement unit and inertial sensor imperfections such as accelerometer biases and gyroscope drifts. Mitigation to this growth and bounding the errors is to update the inertial navigation system periodically with external position (and/or velocity, attitude) fixes. The synergistic effect is obtained through external measurements updating the inertial navigation system using Kalman filter algorithm. It is a natural requirement that the inertial data and data from the external aids be combined in an optimal and efficient manner. In this paper an efficient method for integration of Strapdown Inertia Navigation System (SINS), Global Positioning System (GPS) and Doppler radar is presented using a centralized linear Kalman filter by treating vector measurements with uncorrelated errors as scalars. Two main advantages have been obtained with this improved scheme. First is the reduced computation time as the number of arithmetic computation required for processing a vector as successive scalar measurements is significantly less than the corresponding number of operations for vector measurement processing. Second advantage is the improved numerical accuracy as avoiding matrix inversion in the implementation of covariance equations improves the robustness of the covariance computations against round off errors.

scholarly journals Application of Federal Kalman Filter with Neural Networks in the Velocity and Attitude Matching of Transfer Alignment

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Lijun Song ◽  
Zhongxing Duan ◽  
Bo He ◽  
Zhe Li
Keyword(s):  
Neural Networks ◽  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Estimation Error ◽  
Inertial Navigation ◽  
Transfer Alignment ◽  
The Neural Networks ◽  
Initial Attitude ◽  
Noise Statistics

The centralized Kalman filter is always applied in the velocity and attitude matching of Transfer Alignment (TA). But the centralized Kalman has many disadvantages, such as large amount of calculation, poor real-time performance, and low reliability. In the paper, the federal Kalman filter (FKF) based on neural networks is used in the velocity and attitude matching of TA, the Kalman filter is adjusted by the neural networks in the two subfilters, the federal filter is used to fuse the information of the two subfilters, and the global suboptimal state estimation is obtained. The result of simulation shows that the federal Kalman filter based on neural networks is better in estimating the initial attitude misalignment angle of inertial navigation system (INS) when the system dynamic model and noise statistics characteristics of inertial navigation system are unclear, and the estimation error is smaller and the accuracy is higher.

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